Bitmap cluster analysis of defects in integrated circuits

ABSTRACT

A system and method for defect analysis are disclosed wherein a defect data set is input into the system. A radius value is selected by a user, which is the maximum number of bits that bit failures can be separated from one another to be considered a bit cluster. When a defect data set is received, the system and method start with a fail bit and search for neighboring fail bits. The specified radius is used to qualify the found fail bits to be part of the bit cluster or not. If a minimum count of fail bits is not met, the system and method will stop searching and move to the next fail bit. If a minimum count of fail bits is met, the search continues for the next fail bit until the maximum fail bit count specified by the user is reached. Aggregation is provided such that once bit clusters have been classified, the number of clusters that have the exact match or partial match to each other is counted. The user may set the partial match as a threshold count to establish a match.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to a system and method forquality control of integrated circuits manufactured by a semiconductormanufacturing process and, more particularly, to a system and method foranalyzing defects in integrated circuits manufactured using asemiconductor fabrication process.

2. Description of the Prior Art

The semiconductor manufacturing industry is continually evolving itsfabrication processes and developing new processes to produce smallerand smaller geometries of the semiconductor devices being manufactured,because smaller devices typically generate less heat and operate athigher speeds than larger devices. Currently, a single integratedcircuit chip may contain over one billion patterns. The occurrence ofdefects may cause lower yield in the final semiconductor product, whereyield may be defined as the number of functional devices produced by theprocess as compared to the theoretical number of devices that could beproduced assuming no bad devices.

Improving yield is a critical problem in the semiconductor manufacturingindustry and has a direct economic impact on it. In particular, a higheryield translates into more devices that may be sold by the manufacturer,and greater profits.

Typically, semiconductor manufacturers collect data about variousdefects and analyze the data and, based on data analysis, adjust theintegrated circuit design or process steps or tool specifications in anattempt to improve the yield of the process. This has created a need fora new generation of tools and techniques for defect analysis forsemiconductor yield management.

U.S. Pat. No. 6,470,229 B1 assigned to the same assignee as the presentapplication discloses a yield management system and technique togenerate a yield model. The system can also accept user input to modifythe generated model.

Additionally, a Genesis™ Bitmap Analysis product module is commerciallyavailable from the assignee of U.S. Pat. No. 6,470,229 B1 to extend thecapabilities of the yield management system to direct bitmap-levelanalysis. The Bitmap Analysis product module suite allows a user tograph and analyze bitmap data. Bit failures are revealed to the uservisually with interactive bitmaps. Analysis is performed on classifiedbitmap pattern information imported into a data sheet. Patterns mayconsist of single-bit fails, dual bits, rows, columns, etc. A generalinstance of a fail is described internally preferably using a list ofone or more bounding rectangles to specify the set of bits that failed.Each bounding rectangle is specified by the bit coordinates of thelower-left and upper-right corners of the rectangle.

Information on each of a die's bit failures can be viewed by toggling an“N of (total number of bit fails)” list in a Bit-Fail Browser. Thefollowing information may be displayed for each bit failure:

-   Array—the array where the bit failure occurred.-   Block—the block where the bit failure occurred.-   Pattern—the bit-fail pattern associated with the bit failure. If the    bit failure is not associated with a defined bit-fail pattern, this    field will be grayed out.-   Bit Count—this field specifies the total number of bits that failed    in the defined rectangle associated with the bit-fail pattern. The    rectangle is defined by Array, Block, and physical coordinates (X0,    Y0) and (X1, Y1).-   Sub-Pattern—the index of the bounding rectangle(s) in the bit-fail    pattern. Each sub-pattern has a range.    Range—the X0, X1, Y0, and Y1 coordinates describe the lower-left    corner (X0, Y0) and the upper-right corner (X1, Y1) of the bounding    rectangle associated with the current sub-pattern. These coordinates    are in units of bits from the lower-left corner of the array/block.-   Match—if this option is checked by a user, the bit failure has been    matched to a known defect.-   Reticle Repeater—if this option is checked by the user, the bit    failure is repeating on the same reticle.

Random failed bits are common in memory devices and in embedded memoryin system-on-chip integrated circuits. Failed bits are typicallyclassified into a pattern name using a rigid predefined group of bits.Some examples of common predefined patterns are “single bit,” “pairbits,” “group of bits,” etc. These predefined patterns allow a bitmapclassifier to identify and match a group of bits to the patterns. Thereare two major problems that arise with this technique.

First, random individual bits and groups of bits that fail near eachother are typically caused by the same process event and should beclassified to the same pattern, rather than many individual patterns.Second, these seemingly random individual bits and groups of bits aresometimes systematic in nature, meaning that they almost always failedcertain ways. If the patterns are not defined ahead of time, thesesystematic cluster bits will not be identified in a timely manner. Newmemory design and new memory testing techniques may produce thesesystematic patterns, unknowingly to the user.

Thus, it would be desirable to provide a defect analysis system andmethod which overcome the above limitations and disadvantages ofconventional systems and facilitate bitmap analysis leading to moreeffective quality control. It is to this end that the present inventionis directed. The various embodiments of the bit clustering andaggregation system and method in accordance with the present inventionaddress the two aforementioned problems and provide many advantages overconventional defect bitmap analysis systems and techniques.

SUMMARY OF THE INVENTION

One embodiment of the bit clustering and aggregation system and methodin accordance with the present invention provides many advantages overconventional bitmap analysis systems and techniques, which make the bitclustering and aggregation system and method in accordance with thepresent invention more useful to semiconductor manufacturers. The bitclustering and aggregation system and method in accordance with thevarious embodiments of the present invention identify random andsystematic bitmap failed patterns. The system may be fully automated andis easy to use, so that no extra training is necessary to make use ofthe bit clustering and aggregation system. The system generates anoutput preferably in the form of a bit cluster analysis report that iseasy to interpret and understand.

In accordance with one preferred embodiment of the present invention,clustering is provided such that when defining a bit cluster pattern, auser has the following options: 1) selecting a radius specified by anumber of “good” bits away from another fail bit before the current failbit can be classified as part of the original bit cluster; 2) selectinga minimum count of fail bits in a bit cluster; and 3) selecting amaximum count of fail bits in a bit cluster. One embodiment of the bitclustering and aggregation system and method in accordance with thepresent invention may receive a defect data set. When a defect data setis received, the bit clustering and aggregation system and method inaccordance with the present invention starts with a fail bit andsearches for neighboring fail bits. The bit clustering and aggregationsystem and method use the specified radius to qualify the found failbits to be part of the cluster or not. If the minimum count of fail bitsis not met, the bit clustering and aggregation system and method willstop searching and move to the next fail bit. If the minimum count offail bits is met, the bit clustering and aggregation system and methodwill continue to search for the next fail bit until it reaches themaximum fail bit count specified by the user. Aggregation is providedsuch that once clusters have been classified, the number of clustersthat have the exact match or partial match to each other is counted. Theuser has the option to preferably set the partial match as a thresholdcount to establish a match. The bit clustering and aggregation systemand method in accordance with the present invention provide a defectbitmap analysis tool that is more powerful and flexible thanconventional tools.

The foregoing and other objects, features, and advantages of the presentinvention will become more readily apparent from the following detaileddescription of various embodiments, which proceeds with reference to theaccompanying drawing.

BRIEF DESCRIPTION OF THE DRAWING

The various embodiments of the present invention will be described inconjunction with the accompanying figures of the drawing to facilitatean understanding of the present invention. In the figures, likereference numerals refer to like elements. In the drawing:

FIG. 1 is a diagram illustrating an example of a bit clustering andaggregation system in accordance with one embodiment of the presentinvention implemented on a personal computer;

FIG. 2 is a block diagram illustrating more details of the bitclustering and aggregation system in accordance with the embodiment ofthe invention shown in FIG. 1;

FIG. 3 is a flowchart illustrating an example of a bit clustering andaggregation method in accordance with one embodiment of the presentinvention;

FIG. 4 illustrates an Edit Bitmap Patterns and Layout screen displayedin conjunction with one embodiment of the bit clustering and aggregationsystem and method of the present invention;

FIG. 5 illustrates a screen showing failed bit clusters displayed by oneembodiment of the bit clustering and aggregation system and method ofthe present invention;

FIG. 6 illustrates a Bitmap Analysis Preferences screen displayed by oneembodiment of the bit clustering and aggregation system and method ofthe present invention;

FIG. 7 illustrates a Bit Cluster Analysis Setup screen which appearswhen a user positions the mouse pointer on the “Cluster Analysis” tabillustrated in FIG. 6 and clicks the left mouse button; and

FIG. 8 illustrates a Bitmap Cluster Analysis report displayed by oneembodiment of the bit clustering and aggregation system and method ofthe present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention is particularly applicable to acomputer-implemented software-based defect analysis system and method,and it is in this context that the various embodiments of the presentinvention will be described. It will be appreciated, however, that thedefect analysis system and method in accordance with the presentinvention have greater utility, since they may be implemented inhardware or may incorporate other modules or functionality not describedherein.

FIG. 1 is a block diagram illustrating an example of a defect analysissystem 10 in accordance with one embodiment of the present inventionimplemented on a personal computer 12. In particular, the personalcomputer 12 may include a display unit 14, which may be a cathode raytube (CRT), a liquid crystal display, or the like; a processing unit 16;and one or more input/output devices 18 that permit a user to interactwith the software application being executed by the personal computer.In the illustrated example, the input/output devices 18 may include akeyboard 20 and a mouse 22, but may also include other peripheraldevices, such as printers, scanners, and the like. The processing unit16 may further include a central processing unit (CPU) 24, a persistentstorage device 26, such as a hard disk, a tape drive, an optical disksystem, a removable disk system, or the like, and a memory 28. The CPU24 may control the persistent storage device 26 and memory 28.Typically, a software application may be permanently stored in thepersistent storage device 26 and then may be loaded into the memory 28when the software application is to be executed by the CPU 24. In theexample shown, the memory 28 may contain a bitmap analyzer 30. Thebitmap analyzer 30 may be implemented as one or more softwareapplications that are executed by the CPU 24.

In accordance with the present invention, the defect analysis system 10may also be implemented using hardware and may be implemented ondifferent types of computer systems, such as client/server systems, Webservers, mainframe computers, workstations, and the like. Now, moredetails of an exemplary implementation of the defect analysis system 10in software will be described.

FIG. 2 is block diagram illustrating more details of the bitmap analyzer30 in accordance with one embodiment of the present invention. Inparticular, the bitmap analyzer 30 may receive a defect data setcontaining various types of semiconductor process defect data of theparticular semiconductor device or integrated circuit being inspected.For example, the data may be produced by a memory tester that extractsbits from memory chips or a liquid crystal display (LCD) tester thatextracts bits from an LCD. The bitmap analyzer 30 may process the defectdata set and generate an output that may indicate, for example, thelocation of clusters of defects that affected the yield of the devicesthat generated the current defect data set.

Considered in more detail, as shown in FIG. 2, the defect data set maybe input to a bit clustering and aggregation processor 32 that analyzesthe data and identifies clusters of defects. The user may preferablyenter preferences using a setup module 34 to define the attributes of abit cluster. Once user preferences have been incorporated, an analysisof the defect data set may be automatically performed by the bitclustering and aggregation processor 32. The output of the bitmapanalyzer 30 may be, for example, a listing of one or more clusters offailed bits that affect the yield of the devices that generated thedefect data set being analyzed. Now, a bit clustering and aggregationmethod in accordance with one embodiment of the present invention willbe described.

The bit clustering and aggregation system and method in accordance withthe embodiments of the present invention are preferably used as acompanion to the yield management system and technique disclosed inaforementioned U.S. Pat. No. 6,470,229 B1, the disclosure of which ishereby incorporated in its entirety herein by this reference. Briefdescriptions of the primary functions of the yield management system andtechnique disclosed in aforementioned U.S. Pat. No. 6,470,229 B1, suchas how to use preferences, set up and run analyses, and interpretresults, will be summarized prior to describing using the bitmapanalyzer 30.

The bit clustering and aggregation system and method in accordance withthe preferred embodiment of the present invention preferably perform ananalysis of defect data and generate a report of bit-fail cluster data.FIG. 3 is a flowchart illustrating an example of a bit clustering andaggregation method 40 in accordance one embodiment of the presentinvention. The method may include setting preferences, as indicated by astep 41 shown in FIG. 3, and receiving a defect data set, as indicatedby a step 42 shown in FIG. 3.

As indicated by a step 44 shown in FIG. 3, the defect data set may beanalyzed to identify bit-fail cluster patterns. Once the analysis iscomplete, a report, for example, a Bitmap Cluster Analysis report, canbe generated, as indicated by a step 46 shown in FIG. 3. Each of theabove steps will now be described in more detail to provide a betterunderstanding of the method in accordance with the various embodimentsof the present invention. In particular, the preference setting step 41in accordance with the method of the present invention will now bedescribed.

Cluster patterns must be defined in the Edit Bitmap Patterns and Layoutapplication before a Bitmap Cluster Analysis report can be generated, aswill now be described. In order to edit patterns and layout, names anddescriptions of bit-fail patterns and pattern groups may be created andsaved as Genesis Bitmap Pattern (*.gbp) files. Setup information forarrays and blocks can be created and saved as Genesis Bitmap Layout(*.gbl) files.

A Bitmap Pattern (.gbp) file is comprised of a collection of bitmappattern classifications. A Bitmap Pattern file can consist of as littleas one bitmap pattern, or as many as 30 or more.

Bitmap Pattern (.gbp) files can be attached to bit-fail data sheets, orloaded from a Bitmap Pattern Editor. Bitmap patterns within a .gbp filecan be added and modified from the Bitmap Pattern Editor. Adding bitmappatterns will now be described.

Bitmap patterns are added and modified through the Bitmap PatternEditor. There are numerous steps involved in defining a bitmap pattern,especially if the pattern is complex. The tasks involved in defining abitmap pattern are as follows. Defining a bitmap pattern requires a userto follow the steps below to add a bitmap pattern to the Bitmap Pattern(.gbp) file.

1. From a Bitmap Analysis menu, the user selects Edit Bitmap Patternsand Layout, as shown in FIG. 4. Unless there is an existing .gbp fileattached to the current data sheet, all options on the Edit BitmapPatterns and Layout screen shown in FIG. 4 will be grayed out until apattern is defined, or an existing Bitmap Pattern file is loaded. If anexisting .gbp file is attached to the open data sheet, the Edit BitmapPatterns and Layout screen will load with the attached .gbp file opened.

2. To add a new pattern to the list box, the user clicks the (+) buttonand then inputs a name for the new pattern to be defined. The patternname will be added to the list box, and all options will be availablefor modification. Each additional pattern is added to the list box thesame way. As shown in FIG. 4, the pattern to be added in accordance withthe bit clustering and aggregation system and method in accordance withone embodiment of the present invention is “cluster”.

To duplicate a pattern (same rectangle definition, attributes, andoptions), the user selects the pattern to duplicate in the list box, andthen clicks a Duplicate Pattern button 50. Once the new pattern is givena name, it will appear in the list box. The user can then make anychanges to the new pattern. This time-saving feature can be useful whendefining patterns that are very similar.

To remove a pattern from the list box, the user selects the pattern andthen clicks the (X) button. To rearrange the order of the bitmappatterns, the user selects the pattern and then uses the up and downarrows.

3. Preferably, each bitmap pattern has an associated color. To modifythe color for a pattern, the user clicks a Color box and selects anothercolor from the palette.

4. By default, a Bitmap Classifier searches for bitmap patternsblock-by-block. To direct the Bitmap Classifier to search only forbitmap patterns spanning entire arrays, the user selects an Array-LevelPattern check box. If the Array-Level Pattern option is selected, bitmappatterns that do not span an entire array(s) will not be considered.

To search for array-level bitmap patterns in a specific array, the userinputs (or toggles) that array number into an Array field. If the Arrayfield is set to 0, as shown in FIG. 4, the behavior is to search allarrays in the bitmap.

5. To assign a bitmap pattern to a pattern group (rows, columns, doublecolumns, etc.), the user selects the pattern and then selects thepattern group from the Group 1 or Group 2 drop-down list.

6. The user also sets the frequency range. In accordance with oneembodiment of the bit clustering and aggregation system and method ofthe present invention, the default for Min. Frequency and Max. Frequencyis 0.

7. The user toggles the Destructive Pattern option, according to whetheror not the pattern is destructive, respectively.

8. If desired, Match Conditions may be specified by the user for aspecific defect layer name and defect classification number combination.A bitmap pattern must be found at the specified layer and defectclassification to satisfy the pattern. Wildcards are allowed, andmultiple pairs may be listed if separated by commas. If the Inverseoption is selected by the user, the inverse conditions apply.

Once the initial steps (above) are completed, it is then time to definethe region(s) and all its attributes. Insofar as defining a patternregion is concerned, to define a region for a bitmap pattern, the userfollows the steps below.

1. The user selects the “Cluster” region type. With cluster patterns,the user only sets up the search parameters, and then the clusterpatterns are discovered through the Cluster Analysis application.

2. The scan direction is preselected to be right to left and top tobottom, as indicated by the selected Direction radio button. The scandirection defines the direction in which bits will be scanned.

3. By default, the Min. Count and Max. Count options are set to 0,meaning that every bit contained in the cluster region must be failed tosatisfy the pattern. If the Min. Count is set to 5 and the Max. Count isset to 25, between five and 25 failed bits must exist in the clusterregion to satisfy the pattern.

In the Repeat Attributes section, the Horiz. Repeat Type and Vert.Repeat Type are defaulted to “None”. Also, insofar as the StepAttributes are concerned, the Horiz. Position(s) and Vert. Position(s)fields are left blank, which indicates that all bits will be scanned(starting at bit 0). In the Isolation Type section, the isolation typeis defaulted to “None”. The Two Sub-Patterns option is not available forbitmap clusters.

Additional bitmap pattern options are also available. The user may clickthe Load button to load an existing Bitmap Pattern (.gbp) file. Thepatterns contained in this file can then be modified. Also, the user mayclick the Save button to save the exiting set of patterns (andattributes) as a Bitmap Pattern (.gbp) file. Additionally, the user maydouble-click a pattern name inside the list box to modify the name.Clicking the Erase All button will erase all patterns (and attributes)from the dialog box. However, this action will only affect the current.gbp file if the user “overwrites” that file using the Save feature.

In accordance with the bit clustering and aggregation system and methodin accordance with one embodiment of the present invention with clusterpatterns, the following options are available for a bitmap cluster:

Color—all discovered bitmap clusters will be associated with this color.To modify the color, the user clicks the Color box and selects anothercolor from the palette.

Array (number)—to search for bitmap clusters in a specific array, theuser inputs or toggles that array number into the Array field. If theArray field is set to 0, the behavior is to search all arrays in thebitmap.

(Pattern) Group 1/2—to assign bitmap clusters to a pattern group, theuser selects a pattern group from the Group 1 or Group 2 drop-down list.

Radius—the user inputs or toggles a radius value, which is the maximumnumber of bits that bit failures can be separated from one another to beconsidered a bit cluster.

Min. Count/Max. Count—by default, the Min. Count and Max. Count optionsare set to 0, meaning that every bit contained within the radius valuemust be failed to be considered a cluster. For example, if the Min.Count is set to 4 and the Max. Count is set to 6, between four and sixfailed bits must exist within the radius to be considered a bit cluster.As described above, the user sets up the search parameters, and thenbit-fail cluster patterns that satisfy the parameters are identifiedthrough the Cluster Analysis application.

In summary, the bit clustering and aggregation system and method inaccordance with one embodiment of the present invention provide thefollowing available clustering options:

1. Radius—number of bit spaces between failed bits

2. Min. Count—minimum of number of failed bits in a cluster

3. Max. Count—maximum of number of failed bits in a cluster, as shown inFIGS. 4 and 5.

The bit clustering and aggregation system and method in accordance withone embodiment of the present invention preferably provide anaggregation option so that the user may specify a number of matchesrequired. The user specifies the number of matches using ClusterAnalysis preferences. To modify Cluster Analysis preferences, the userperforms the following steps:

1. From the Edit menu, select Edit Preferences→Bitmap AnalysisPreferences, as shown in FIG. 6. Position the mouse pointer on the“Cluster Analysis” tab, and click. When the user clicks the “ClusterAnalysis” tab, a Bit Cluster Analysis Setup screen appears, as shown inFIG. 7, to display cluster analysis preferences.

2. Input (or use toggle keys) an integer value for the matchesThreshold. A bit-fail cluster must have at least this many matches to beincluded in the Bitmap Cluster Analysis report.

3. Click OK to save preference settings.

The count Threshold shown in FIG. 7 is the number of matches requiredbefore the cluster is displayed in an analysis report. In onecontemplated modification in accordance with the present invention, theuser may be provided the alternative option to select a “Match %Threshold” which would correspond to a percentage of match of eachcluster.

The user may now receive a defect data set, initiate a Bitmap ClusterAnalysis, and create a Bitmap Cluster Analysis report, as indicated bythe steps 42, 44 and 46 shown in FIG. 3, respectively. To initiate aBitmap Cluster Analysis, the user performs the following steps:

1. Select Cluster Analysis from the Bitmap Analysis Preferences menu.The Bitmap Cluster Analysis Setup dialog box shown in FIG. 7 appears.

2. Select one parameter from the Bit-Fail Parameter list box, e.g.,“14220”, as shown in FIG. 7. Pattern text matching is available. Theuser may right-click the list box to perform a search. The user mayalternatively click the F7 key on the keyboard 20 to restore theoriginal list of parameters. Also, the user may click the F6 key on thekeyboard 20 to restrict the list to marked parameters. To sort theparameters in alphanumerical order, the user may click the F8 key on thekeyboard 20.

3. To modify the count Threshold, input (or use toggle keys) to selectan integer value for the matches Threshold, as described earlier. Abit-fail cluster must have at least this many matches to be included inthe Bitmap Cluster Analysis report.

4. Click OK to generate a Bitmap Cluster Analysis report.

Preferably, the resulting output of the bit clustering and aggregationsystem and method in accordance with one embodiment of the presentinvention is a Bitmap Cluster Analysis report to display Bitmap ClusterAnalysis results. An example of a Bitmap Cluster Analysis report isshown in FIG. 8. All bit-fail cluster patterns that satisfied thecluster definition appear in the report. The bit-fail cluster patternsare preferably listed in descending order according to the number ofmatches. Each column is described below:

Cluster—shows a visualization of the cluster pattern.

# Matches—the number of bit-fail patterns that match the clusterpattern.

# Bits—the number of bits that constitute the pattern.

Lot—lot identification (ID).

Wafer—wafer ID.

Die_X—X-coordinate for the die location in the wafer map.

Die_Y—Y-coordinate for the die location in the wafer map.

Array—array in which the die appears.

Block—block in which the die appears.

X0—the X0-coordinate for the bit location in the die.

Y0—the Y0-coordinate for the bit location in the die.

X1—the X1-coordinate for the bit location in the die.

Y1—the Y1-coordinate for the bit location in the die.

Finally, if the user right-clicks anywhere in the Bitmap ClusterAnalysis report, a pop-up menu preferably appears. The following threeoptions are preferably available with the pop-up menu:

Find Text—opens the Find Text dialog box. Input the search string in theFind Text field, or select a previously searched string from thedrop-down list. For a case-sensitive search, select the Case Sensitivecheck box. For a regular expression search, select the RegularExpression check box. Click the Find Next button to find the first/nextoccurrence of the search string.

Save Report—report can be saved as a tab-delimited text file, a Genesisdata sheet (*.gds) file, or an HTML file.

Re-Display Setup Dialog—re-displays the Bitmap Cluster Analysis Setupdialog box shown in FIG. 7 with the same settings used to generatecurrent report.

While the foregoing description has been with reference to particularembodiments of the present invention, it will be appreciated by thoseskilled in the art that changes in these embodiments may be made withoutdeparting from the principles and spirit of the invention, the scope ofwhich is defined by the appended claims.

1. A bit cluster analysis system to identify random and systematicbitmap failed patterns, comprising: means for defining bit clusterscomprising means for selecting a radius which is specified by a maximumnumber of bits that a fail bit can be separated from another fail bit tobe classified a bit cluster; means for receiving a defect data setcomprising fail bits; means for starting with a fail bit and finding aneighboring fail bit using the radius to qualify the found neighboringfail bit to be part of the bit cluster if the found neighboring fail bitis within the radius; and means for aggregating a plurality of bitclusters by identifying one or more bit cluster patterns from theplurality of bit clusters that have a match to each other.
 2. The systemof claim 1, further comprising means for generating a report.
 3. Thesystem of claim 2 wherein the report is a bit cluster analysis report.4. The system of claim 3, further comprising means for counting thenumber of bit clusters that have a match to each other once clustershave been classified and wherein bit-fail cluster patterns are listed indescending order according to the number of matches.
 5. The system ofclaim 1 wherein the means for defining bit clusters further comprisesmeans for selecting a minimum count of fail bits in a bit cluster,whereby if the minimum count of fail bits is not met, the system willstop searching and move to a next fail bit.
 6. The system of claim 1wherein the means for defining bit clusters further comprises means forselecting a maximum count of fail bits in a bit cluster, whereby if theminimum count of fail bits is met, the system will continue to searchfor a next fail bit until the system reaches the maximum count of failbits.
 7. The system of claim 1, further comprising means for counting anumber of bit clusters associated with a bit cluster pattern from theone or more bit cluster patterns.
 8. The system of claim 1 wherein thedefect data set is produced by a memory tester that extracts bits frommemory chips.
 9. The system of claim 1 wherein the defect data set isproduced by a liquid crystal display tester that extracts bits from aliquid crystal display.
 10. The system of claim 1, wherein the defectdata set is produced by an integrated circuit tester that extracts bitdata from chips.
 11. The system of claim 1, wherein the match is anexact match.
 12. The system of claim 11, wherein the means foraggregating identifies that a bit cluster from the plurality of bitclusters has an exact match to a bit cluster pattern from the one ormore bit cluster patterns if every bit of the bit cluster is failed. 13.The system of claim 1, wherein the match is a partial match.
 14. Thesystem of claim 13, wherein the means for aggregating identifies that abit cluster from the plurality of bit clusters has a partial match to abit cluster pattern from the one or more bit cluster patterns if: thebit cluster satisfies a predetermined minimum fail bit count associatedwith the bit cluster pattern; and the bit cluster satisfies apredetermined maximum fail bit count associated with the bit clusterpattern.
 15. The system of claim 1, further comprising means for a userto define a threshold count to establish a match.
 16. The system ofclaim 1, wherein the one or more bit cluster patterns span an array. 17.The system of claim 1, wherein the one or more bit cluster patterns arein a specific array.
 18. A bit cluster analysis method to identifyrandom and systematic bitmap failed patterns, comprising the steps of:defining bit clusters with a bit cluster analysis system, comprisingselecting a radius which is specified by a maximum number of bits that afail bit can be separated from another fail bit to be classified a bitcluster; receiving, with the bit cluster analysis system, a defect dataset; starting with a fail bit, with the bit cluster analysis system, andfinding a neighboring fail bit using the radius to qualify the foundneighboring fail bit to be part of the bit cluster if the foundneighboring fail bit is within the radius; and aggregating a pluralityof bit clusters, with the bit cluster analysis system, by identifyingone or more bit cluster patterns from the plurality of bit clusters thathave a match to each other.
 19. The method of claim 18, furthercomprising the step of generating a report.
 20. The method of claim 19wherein the report is a bit cluster analysis report.
 21. The method ofclaim 20, further comprising the step of counting the number of bitclusters that have a match to each other once clusters have beenclassified and wherein bit-fail cluster patterns are listed indescending order according to the number of matches.
 22. The method ofclaim 18 wherein the step of defining bit clusters further comprisesselecting a minimum count of fail bits in a bit cluster, whereby if theminimum count of fail bits is not met, the method will stop searchingand move to a next fail bit.
 23. The method of claim 18 wherein the stepof defining bit clusters further comprises selecting a maximum count offail bits in a bit cluster, whereby if the minimum count of fail bits ismet, the method will continue to search for a next fail bit until themethod reaches the maximum count of fail bits.
 24. The method of claim18, further comprising the step of counting a number of bit clustersassociated with a bit cluster pattern from the one or more bit clusterpatterns.
 25. The method of claim 24, further comprising the step ofenabling a user to set a threshold count to establish a partial match.26. The method of claim 18 wherein the defect data set is produced by anintegrated circuit tester that extracts bits from chips that contain amemory circuit block.
 27. The method of claim 18 wherein the defect datais produced by a liquid crystal display tester that extracts bits from aliquid crystal display.
 28. A computer program product, tangiblyembodied in a computer readable medium, the computer program productincluding instructions being operable to cause a data processingapparatus to: define bit clusters comprising selecting a radius which isspecified by a maximum number of bits that a fail bit can be separatedfrom another fail bit to be classified a bit cluster; receive a defectdata set; start with a fail bit and find a neighboring fail bit usingthe radius to qualify the found neighboring fail bit to be part of thebit cluster if the found neighboring fail bit is within the radius; andaggregate a plurality of bit clusters by identifying one or more bitcluster patterns from the plurality of bit clusters that have a match toeach other.
 29. A bit cluster analysis system to identify random andsystematic bitmap failed patterns, the system comprising: a setup moduleconfigured to define bit clusters comprising selecting a radius which isspecified by a maximum number of bits that a fail bit can be separatedfrom another fail bit to be classified a bit cluster; and a bitclustering and aggregation processor configured to: receive a defectdata set; start with a fail bit and find a neighboring fail bit usingthe radius to qualify the found neighboring fail bit to be part of thebit cluster if the found neighboring fail bit is within the radius; andaggregate a plurality of bit clusters by identifying one or more bitcluster patterns from the plurality of bit clusters that have a match toeach other.
 30. A bit cluster analysis system to identify random andsystematic bitmap failed patterns, comprising: means for defining bitclusters comprising: means for selecting a radius which is specified bya maximum number of bits that a fail bit can be separated from anotherfail bit to be classified a bit cluster; and means for selecting aminimum count of fail bits in the bit cluster; means for receiving adefect data set comprising fail bits; means for starting with a fail bitand finding a neighboring fail bit using the radius to qualify the foundneighboring fail bit to be part of the bit cluster if the foundneighboring fail bit is within the radius, whereby the system will stopsearching and move to a next fail bit if the minimum count of fail bitsis not met; and means for aggregating a plurality of bit clusters byidentifying one or more bit cluster patterns from the plurality of bitclusters that have a match to each other.
 31. A bit cluster analysissystem to identify random and systematic bitmap failed patterns,comprising: means for defining bit clusters comprising: means forselecting a radius which is specified by a maximum number of bits that afail bit can be separated from another fail bit to be classified a bitcluster; and means for selecting a minimum count of fail bits in the bitcluster; means for selecting a maximum count of fail bits in the bitcluster; means for receiving a defect data set comprising fail bits;means for starting with a fail bit and finding a neighboring fail bitusing the radius to qualify the found neighboring fail bit to be part ofthe bit cluster if the found neighboring fail bit is within the radius,whereby if the minimum count of fail bits is met, the system willcontinue to search for a next fail bit until the system reaches themaximum count of fail bits; and means for aggregating a plurality of bitclusters by identifying one or more bit cluster patterns from theplurality of bit clusters that have a match to each other.